Gap analysis (conservation)

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Gap analysis is a tool used in wildlife conservation to identify gaps in conservation lands (e.g., protected areas and nature reserves) or other wildlands where significant plant and animal species and their habitat or important ecological features occur. [1]

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Conservation managers or scientists can use it as a basis for providing recommendations to improve the representativeness of nature reserves or the effectiveness of protected areas so that these areas provide the best value for conserving biological diversity. With the information that a gap analysis yields, the boundaries of protected areas may be designed to subsume 'gaps' containing significant populations of wildlife species that can enhance the long-term survival of a larger metapopulation of the species already within the managed or protected area, or to include a diversity of wildlife species or ecosystems that merit protection but are inadequately represented in an existing protected area network. Gap assessments can be done using the geographic information system: land maps that delineate topography, biological and geological features (forest cover, plains, rivers, etc.), boundaries, land ownership and use are overlaid with the distribution of wildlife species. How much of the species' distribution fall within or without the conservation lands, or within a highly exploited area etc. can be identified.

At its simplest, a gap analysis is an assessment of the extent to which a protected area system meets protection goals set by a nation or region to represent its biological diversity. Gap analyses can vary from simple exercises based on a spatial comparison of biodiversity with existing protected areas to complex studies that need detailed data gathering and analysis, mapping and use of software decision packages.

Gap types

Gap analyses generally consider a range of different “gaps” in a protected area network: [2] [3]

Citizen Science in Gap Analysis

Citizen science efforts can contribute valuable data toward the recognition of representation, ecological, and management gaps in conservation and restoration efforts that may have otherwise been costly or labor-intensive for researchers or institutions to undertake.

In a gap analysis evaluating the effectiveness of protected areas for the preservation of the short-snouted seahorse ( Hippocampus hippocampus ) and long-snouted seahorse ( H. guttalatus ) along the Italian coast, researchers used data collected through iSeahorse, a part of Project Seahorse. This tool enables divers from around the world, including ecotourists, to contribute photographs and observations of seahorse species. By combining this citizen-sourced data with geographic information systems (GIS) and species distribution models, researchers were able to identify a representation gap, estimating that only 25-30% of the habitat where these species were spotted was currently under protection by existing conservation areas. [4]

In another study, researchers completed a gap analysis for the preservation of the critically endangered Harpy Eagle ( Harpia harpyja ). The researchers used data sourced from eBird, an application that allows citizens to contribute photographs and observations of avian sightings. Incorporating information gathered from eBird, the species' predicted habitat, and a species distribution model, the researchers concluded that the Harpy Eagle's current designated conservation areas covered approximately 18% of its potential range. [5]

In Argentina, the non-governmental organization Aves Argentinas assembled volunteers to conduct annual bird surveys of the threatened Yellow Cardinal ( Gubernatrix cristata ) from September to October each year from 2015 to 2017. These volunteers recorded observations of the Yellow Cardinal and its nesting sites, providing valuable data to researchers completing a gap analysis aimed at understanding changes in the species' habitat selection over time to assess the adequacy of existing protected areas. This analysis supported researchers in identifying the variations between the current habitats and protected zones. [6]

U.S. Gap Analysis Project

The gap analysis process itself was conceived in the 1980s, by J. Michael Scott, at the University of Idaho. He developed methods to assess endangered birds in Hawaii and began by mapping the distribution of each species individually. Then he combined data on individual species to create a map of species richness throughout the island. Until this approach was developed there was no broad scale way to assess the level of protection given to areas rich in biodiversity. The results of this analysis led to creation of the Hakaiau Forest National Wildlife Refuge, in one of the areas of highest species richness. In the late 1980s, Scott and other researchers at the University of Idaho Cooperative Fish and Wildlife Research Unit initiated an Idaho Gap Analysis Project as a first pilot project under the auspices of the U.S. Fish and Wildlife Service. Following two years of methods development, the program was launched in 1989 as part of the U.S. Geological Survey under the title Gap Analysis Program (GAP). GAP is now known as the Gap Analysis Project. [7]

The Gap Analysis Project mission is to provide state, regional, and national biodiversity assessments of the conservation status of native vertebrate species, aquatic species, and natural land cover types and to facilitate the application of this information to land management activities. The stated goal of GAP is “keeping common species common”. GAP partners in the development of four core datasets: a detailed map of the terrestrial ecosystems of the United States; maps of predicted habitat distributions for the terrestrial vertebrate species for the U.S.; distribution models for aquatic species; and the Protected Areas Database of the U.S. [8]

Critiques and limitations

Threat indicators, scale dependence & the 'modifiable areal unit problem'

Indicators of human threats, such as population growth, land use, and road density have been proposed to enhance gap analysis and further prioritize which ‘gaps’ are most immediately threatened. However, because species responses to threats vary, gap analysis can only portray potential threats. Indicators of conservation value, such as species richness, have no inherent spatial scale. Thus, the optimal scale range for the minimum mapping unit (MMU) is determined on a case-by-case basis, compromising scientific credibility with data availability and cost effectiveness. Scale dependence of the MMU as a variant of the ‘modifiable areal unit problem’, or MAUP. [9] The larger the MMU, the more species it will contain, either over-generalizing species richness by using large units or increasing statistical uncertainty for habitat distributions by using small units. Scale dependence introduces statistical error in spatial analysis.

Mapping uncertainty

Predicted species habitat distributions in GAP data contain numerous errors of commission (attributing presence where a species is absent) and errors of omission (attributing absence where a species is present) resulting in large composite error when map layers are combined. Despite this fact, species distribution maps produced by gap analysis rarely incorporate error into the visual representation. In gap analysis applications, it can result in dramatically different conservation recommendations. [10] In addition, residual multiscale sampling effects can be identified using a statistical covariation measure, such as sensitivity analysis.

The ‘shifting baseline syndrome’

The baseline for all National GAP projects is determined by the satellite data used to determine the vegetation cover that predicts species habitat distribution, which already includes a large percentage of anthropogenic land uses. First, because historic species distribution is not known, gap analysis results are a mere fraction of any species original habitat. Also, the static nature of gap analysis currently is not able to show the dynamic response capacity of species to change or species viability over time. [11] Shifting baselines require that gap analysis incorporates a case-by-case consideration of management goals and definitions of conservation success.

Related Research Articles

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An ecoregion is an ecologically and geographically defined area that is smaller than a bioregion, which in turn is smaller than a biogeographic realm. Ecoregions cover relatively large areas of land or water, and contain characteristic, geographically distinct assemblages of natural communities and species. The biodiversity of flora, fauna and ecosystems that characterise an ecoregion tends to be distinct from that of other ecoregions. In theory, biodiversity or conservation ecoregions are relatively large areas of land or water where the probability of encountering different species and communities at any given point remains relatively constant, within an acceptable range of variation . Ecoregions are also known as "ecozones", although that term may also refer to biogeographic realms.

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<span class="mw-page-title-main">Conservation biology</span> Study of threats to biological diversity

Conservation biology is the study of the conservation of nature and of Earth's biodiversity with the aim of protecting species, their habitats, and ecosystems from excessive rates of extinction and the erosion of biotic interactions. It is an interdisciplinary subject drawing on natural and social sciences, and the practice of natural resource management.

<span class="mw-page-title-main">Landscape ecology</span> Science of relationships between ecological processes in the environment and particular ecosystems

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<span class="mw-page-title-main">Urban ecology</span> Scientific study of living organisms

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<span class="mw-page-title-main">Ecological restoration</span> Scientific study of renewing and restoring ecosystems

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References

  1. ↑ Scott, J.M. and Schipper, J. 2006. Gap analysis: a spatial tool for conservation planning. Pp. 518-519 in M.J. Groom, G.K. Meffe, C. Ronald Carroll and Contributors. Principles of Conservation Biology (3rd ed.). Sunderland, MA: Sinauer.
  2. ↑ Tisdell, C., Wilson, C. and Swarna Nantha, H. 2005. Policies for saving a rare Australian glider: economics and ecology. Biological Conservation 123(2): 237-248.
  3. ↑ Fearnside, P.M. and Ferraz, J. 1995. A conservation gap analysis of Brazil's Amazonian vegetation. Conservation Biology 9(5): 1134-1147.
  4. ↑ Bosso, Luciano; Panzuto, Raffaele; Balestrieri, Rosario; Smeraldo, Sonia; Chiusano, Maria Luisa; Raffini, Francesca; Canestrelli, Daniele; Musco, Luigi; Gili, Claudia (2024-03-01). "Integrating citizen science and spatial ecology to inform management and conservation of the Italian seahorses". Ecological Informatics. 79: 102402. Bibcode:2024EcInf..7902402B. doi:10.1016/j.ecoinf.2023.102402. ISSN   1574-9541.
  5. ↑ Sutton, Luke J; Anderson, David L; Franco, Miguel; McClure, Christopher J W; Miranda, Everton B P; Vargas, F Hernan; Vargas Gonzalez, Jose de J; Puschendorf, Robert (3 May 2022). "Range-wide habitat use of the Harpy Eagle indicates four major tropical forest gaps in the Key Biodiversity Area network". Ornithological Applications. doi:10.1093/ornithapp/duac019 . Retrieved 2024-04-18.
  6. ↑ DomíNguez, Marisol; Lapido, RocíO; Gorrindo, AdriáN; Archuby, Diego; Correa, Emilio; Llanos, FabiáN; Reales, Fabricio; Piantanida, Fabrizio; Marateo, GermáN; Meriggi, Jorge; Andreani, Lucas; Encabo, Manuel; Vinassa, MaríA Laura GóMez; Bertini, Maximiliano; Perelló, Milton (March 2021). "A citizen science survey discloses the current distribution of the endangered Yellow Cardinal Gubernatrix cristata in Argentina". Bird Conservation International. 31 (1): 139–150. doi:10.1017/S0959270920000155. ISSN   0959-2709.
  7. ↑ PD-icon.svg This article incorporates public domain material from Gap Analysis Project. History. United States Geological Survey . Retrieved April 16, 2022.
  8. ↑ PD-icon.svg This article incorporates public domain material from Gap Analysis Project. Mission. United States Geological Survey . Retrieved April 16, 2022.
  9. ↑ Stoms, David M. 1994. “Scale dependence of species richness maps.” Professional Geographer. 46(3): 346-358.
  10. ↑ Flather, Curtis H., Kenneth R. Wilson, Denis J. Dean, and William C. McComb. (1997). “Identifying gaps in conservation networks: of indicators and uncertainty in geographic-based analyses.” Ecological Applications. 7(2): 531-542.
  11. ↑ Jennings, Michael J. (2000). “Gap analysis: concepts, methods, and recent results.” Landscape Ecology. 15: 5-20.
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